Matchms

If you're doing metabolomics and need to identify unknown compounds from mass spectra, this handles the grunt work of loading data from mzML, MGF, or MSP files, cleaning it up with 40+ filters, and running similarity searches against spectral libraries. The modified cosine similarity accounts for precursor mass differences, which matters for fragment matching. It's built for reproducible pipelines, so you can chain filters together and process entire libraries consistently. Note this is for small molecule metabolomics, if you're doing proteomics LC-MS/MS work, you want pyopenms instead. The SpectrumProcessor class makes it easy to standardize everything before similarity scoring.

skills.sh installs: 2K
GitHub stars: 56.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill matchms --agent claude-code

Installs into .claude/skills of the current project.

Files

Matchms

Purpose and Scope

Matchms is a Python package for importing, cleaning, processing, and comparing tandem mass spectra. This skill targets matchms 0.33.1, released 2026-06-08, verified again on 2026-10-01. Older tutorials can use removed names; see the migration reference.

Use matchms for:

MS/MS library search and query-versus-reference scoring
Metadata harmonization, adduct/precursor handling, and peak filtering
Cosine, modified-cosine, neutral-loss, approximate, and entropy scoring
Structured score matrices, top-hit extraction, and spectral networks
MGF, MSP, mzML, mzXML, JSON, mzSpecLib, and metabolomics-USI workflows

Do not use matchms as a replacement for:

LC-MS feature detection, chromatographic alignment, peptide identification, or protein quantification — use pyopenms
Vendor raw-file conversion — convert to mzML/mzXML first
A validated compound-identification protocol — similarity is evidence, not proof of identity